Vehicle road side location of a target via unwrapped differential phase rf signals

ABSTRACT

A vehicle side target location method includes receiving a request, via a first wireless connection, for the vehicle to travel to a location, in response to the vehicle being less than a predetermined distance from the location, receiving RF packets, via a second wireless connection having a multiple antenna radio frequency (RF) transceiver having an identification (ID), from a target at the location, identifying packets based on the ID of the RF transceiver, extracting channel state information (CSI) from received signals associated with the identified packets, determining a phase difference of subcarrier phase data of the received signals between each of the multiple antennae, filtering noise of the phase difference of subcarriers based on subcarrier selection to obtain multiple robust phase difference signals, and feeding the multiple robust phase difference signals to a classifier to obtain a side of the vehicle associated with the location of the target.

TECHNICAL FIELD

This disclosure relates generally to use of radio frequency (RF) signalsto locate a target.

BACKGROUND

The increase in hailing services use along with autonomous andsemi-autonomous vehicles has increased a reliance on shared mobility(e.g., Uber, Lyft). However, often drivers and riders or drivers andconsumers (e.g., delivery vehicles) have difficulties finding each otherin urban areas. These difficulties include GPS signals being blocked bybuildings such as skyscrapers, attenuated in crowded environments (e.g.,in stadiums, airports, bars), at night, and in bad weather. Due to thedifficulty in locating each other, the experience may waste time,creates a bad user experience, and causes more wasted energy due to idletime of the vehicle while trying to locate its target.

SUMMARY

In one embodiment, a system for a vehicle includes a multiple antennaradio frequency (RF) transceiver having an identification (ID), and aprocessor coupled with the RF transceiver. The processor is configuredto receive a request, via a first wireless connection, for the vehicleto travel to a location, in response to the vehicle being less than apredetermined distance from the location, receive RF packets, via asecond wireless connection, from a target at the location, identifypackets based on the ID of the RF transceiver, extract channel stateinformation (CSI) from received signals associated with the identifiedpackets, unwrap phase from the CSI of the received signals to obtainsubcarrier phase data, determine a phase difference of subcarriers ofthe received signals between each of the multiple antennae, filter noiseof the phase difference based on subcarrier selection to obtain multiplerobust phase difference signals, and feed the multiple robust phasedifference signals to a classifier to obtain a side of the vehicleassociated with the location of the target.

In another embodiment, a vehicle side target location method includesreceiving a request, via a first wireless connection, for the vehicle totravel to a location, in response to the vehicle being less than apredetermined distance from the location, receiving RF packets, via asecond wireless connection having a multiple antenna radio frequency(RF) transceiver having an identification (ID), from a target at thelocation, identifying packets based on the ID of the RF transceiver,extracting channel state information (CSI) from received signalsassociated with the identified packets, unwrapping phase from the CSI ofthe received signals to obtain subcarrier phase data, determining aphase difference of subcarrier phase data of the received signalsbetween each of the multiple antennae, filtering noise of the phasedifference of subcarriers based on subcarrier selection to obtainmultiple robust phase difference signals, and feeding the multiplerobust phase difference signals to a classifier to obtain a side of thevehicle associated with the location of the target.

In another embodiment, a system for performing lane selection associatedwith autonomous control of a vehicle includes a multiple antenna radiofrequency (RF) transceiver having an identification (ID), a processorcoupled with the RF transceiver, and a memory including instructions.When the instructions are executed by the processor, it causes theprocessor to receive a request, via a first wireless connection, for thevehicle to travel to a location, in response to the vehicle being lessthan a predetermined distance from the location, receive RF packets, viaa second wireless connection, from a target at the location, identifypackets based on the ID of the RF transceiver, extract channel stateinformation (CSI) from received signals associated with the identifiedpackets, determine a phase difference of subcarriers of the receivedsignals between each of the multiple antennae, filter noise of the phasedifference of subcarriers based on subcarrier selection to obtainmultiple robust phase difference signals, feed the multiple robust phasedifference signals to a classifier to obtain a side of the vehicleassociated with the location of the target, and operate the vehicle tonavigate the vehicle to a lane associated with the side of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary configuration within which thesystems and methods disclosed herein could be implemented according tosome embodiments of the present disclosure.

FIG. 2 is an illustration of a vehicle approaching a target rider.

FIG. 3 is a flow diagram of various embodiments of the presentdisclosure.

FIG. 4 is a flow diagram of an amplitude-based target side of the roaddetermination.

FIG. 5 is a flow diagram of a LSTM based binary classifier.

FIG. 6A is a graphical representation of amplitude of two antennas whena target is at the right side.

FIG. 6B is a graphical representation of amplitude difference betweentwo antennas when a target is at the right side.

FIG. 7A is a graphical representation of amplitude of two antennas whena target is at the left side.

FIG. 7B is a graphical representation of amplitude difference betweentwo antennas when a target is at the left side.

FIG. 8 is a flow diagram of phase-based target side of the roaddetermination.

FIG. 9 is a flow diagram of synchronized phase unwrapping based targetside of the road determination.

FIG. 10 is a graphical representation of unwrapped phase of two antennaswhen a target is at the right side.

FIG. 11A is a graphical representation of Positive and negative votes ofunwrapped phase difference between antennas when the rider is on theleft side.

FIG. 11B is a graphical representation of Positive and negative votes ofunwrapped phase difference between antennas when the rider is on theright side.

FIG. 12 is a graphical representation of estimating effective phasedifference between antennas (C−A) when the rider is at the right side inLoS.

FIG. 13A is a flow diagram of RSSI-based target range determination.

FIG. 13B is another flow diagram of RSSI-based target rangedetermination.

FIG. 14A is a graphical representation of a box plot of RSSI atdifferent distances between a target and vehicle when the target is onthe right side.

FIG. 14B is a graphical representation of a box plot of RSSI atdifferent distances between a target and vehicle when the target is onthe left side.

FIG. 15 is a block diagram illustrating a computing device showing anexample of a device used in various embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of non-limiting illustration, certain exampleembodiments. Subject matter may, however, be embodied in a variety ofdifferent forms and, therefore, covered or claimed subject matter isintended to be construed as not being limited to any example embodimentsset forth herein; example embodiments are provided merely to beillustrative. Likewise, a reasonably broad scope for claimed or coveredsubject matter is intended. Among other things, for example, subjectmatter may be embodied as methods, devices, components, or systems.Accordingly, embodiments may, for example, take the form of hardware,software, firmware or any combination thereof (other than software perse). The following detailed description is, therefore, not intended tobe taken in a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The present disclosure is described below with reference to blockdiagrams and operational illustrations of methods and devices. It isunderstood that each block of the block diagrams or operationalillustrations, and combinations of blocks in the block diagrams oroperational illustrations, can be implemented by means of analog ordigital hardware and computer program instructions. These computerprogram instructions can be provided to a processor of a general purposecomputer to alter its function as detailed herein, a special purposecomputer, ASIC, or other programmable data processing apparatus, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, implement thefunctions/acts specified in the block diagrams or operational block orblocks. In some alternate implementations, the functions/acts noted inthe blocks can occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession can in factbe executed substantially concurrently or the blocks can sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

For the purposes of this disclosure a non-transitory computer readablemedium (or computer-readable storage medium/media) stores computer data,which data can include computer program code (or computer-executableinstructions) that is executable by a computer, in machine readableform. By way of example, and not limitation, a computer readable mediummay comprise computer readable storage media, for tangible or fixedstorage of data, or communication media for transient interpretation ofcode-containing signals. Computer readable storage media, as usedherein, refers to physical or tangible storage (as opposed to signals)and includes without limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid state memory technology, optical storage,cloud storage, magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

For the purposes of this disclosure the term “server” should beunderstood to refer to a service point which provides processing,database, and communication facilities. By way of example, and notlimitation, the term “server” can refer to a single, physical processorwith associated communications and data storage and database facilities,or it can refer to a networked or clustered complex of processors andassociated network and storage devices, as well as operating softwareand one or more database systems and application software that supportthe services provided by the server. Cloud servers are examples.

For the purposes of this disclosure a “network” should be understood torefer to a network that may couple devices so that communications may beexchanged, such as between a server and a client device or other typesof devices, including between wireless devices coupled via a wirelessnetwork, for example. A network may also include mass storage, such asnetwork attached storage (NAS), a storage area network (SAN), a contentdelivery network (CDN) or other forms of computer or machine readablemedia, for example. A network may include the Internet, one or morelocal area networks (LANs), one or more wide area networks (WANs),wire-line type connections, wireless type connections, cellular or anycombination thereof. Moreover, a network can also refer to an automotivenetwork, such as, for example, a network where the nodes are vehicles(or autonomous vehicles), a network where the nodes are vehicles and theserver is a remote computer in a cloud infrastructure, and the like.Likewise, sub-networks, which may employ differing architectures or maybe compliant or compatible with differing protocols, may interoperatewithin a larger network.

For purposes of this disclosure, a “wireless network” should beunderstood to couple client devices with a network. A wireless networkmay employ stand-alone ad-hoc networks, mesh networks, Wireless LAN(WLAN) networks, cellular networks, or the like. A wireless network mayfurther employ a plurality of network access technologies, includingWi-Fi, Long Term Evolution (LTE), WLAN, Wireless Router (WR) mesh, or2nd, 3rd, 4^(th) or 5^(th) generation (2G, 3G, 4G or 5G) cellulartechnology, mobile edge computing (MEC), Bluetooth, 802.11b/g/n, or thelike. Network access technologies may enable wide area coverage fordevices, such as client devices with varying degrees of mobility, forexample. A long-range transceiver include systems intended to be capableand practical in communication over a distance of greater than 100meters, examples include satellite and cellular (analog, CDMA, TDMA, 2G,3G, 4G, LTE, 5G, etc.), while a medium-range transceiver include systemsdesigned to operate distances less than 100 m, examples include 802.11Wi-Fi, Bluetooth, Ultra-wide band (UWB).

In short, a wireless network may include virtually any type of wirelesscommunication mechanism by which signals may be communicated betweendevices, such as a client device or a computing device, between orwithin a network, or the like.

A computing device may be capable of sending or receiving signals, suchas via a wired or wireless network, or may be capable of processing orstoring signals, such as in memory as physical memory states, and may,therefore, operate as a server. Thus, devices capable of operating as aserver may include, as examples, dedicated rack-mounted servers, desktopcomputers, laptop computers, set top boxes, integrated devices combiningvarious features, such as two or more features of the foregoing devices,or the like.

For purposes of this disclosure, a client (or consumer or user ormobile) device, referred to as user equipment (UE)), may include acomputing device capable of sending or receiving signals, such as via awired or a wireless network. A client device may, for example, include adesktop computer or a portable device, such as a cellular telephone, asmart phone, a display pager, a radio frequency (RF) device, an infrared(IR) device a Near Field Communication (NFC) device, a Personal DigitalAssistant (PDA), a handheld computer, a tablet computer, a phablet, alaptop computer, a set top box, a wearable computer, smart watch, anintegrated or distributed device combining various features, such asfeatures of the forgoing devices, or the like.

A client device (UE) may vary in terms of capabilities or features. Thedisclosed (and claimed) subject matter is intended to cover a wide rangeof potential variations, such as a web-enabled client device orpreviously mentioned devices that may include a high-resolution screen(HD or 4K for example), one or more physical or virtual keyboards, massstorage, one or more accelerometers, one or more gyroscopes, globalpositioning system (GPS) or other location-identifying type capability,or a display with a high degree of functionality, such as atouch-sensitive color 2D or 3D display, for example. Moreover, thedisclosed (and claimed) subject matter is also intended to cover devicesthat utilize, rely on and/or incorporate automotive electronic controlunits (ECUs), automotive gateways, vehicle computers, and/or any othertype of known or to be known component that can part of an electronicvehicle (EV) and/or its operating system.

With reference to FIG. 1 , system (or framework) 100 is depicted whichincludes UE 106 (e.g., a client device or mobile device), network 102,cloud system 104 and vehicle 108 (e.g., a vehicle with an embeddedclient device or embedded mobile device). UE 106 can be any type ofdevice, such as, but not limited to, a mobile phone, tablet, laptop,personal computer, sensor, Internet of Things (IoT) device, autonomousmachine, and any other device equipped with a cellular or wireless orwired transceiver. Further discussion of UE 106 and vehicle 108 isprovided below at least in reference to FIG. 15 .

Network 102 can be any type of network, such as, but not limited to, awireless network, cellular network, the Internet, automotive network,and the like (as discussed above). As discussed herein, network 102 canfacilitate connectivity of the components of system 100, as illustratedin FIG. 1 .

Cloud system 104 can be any type of cloud operating platform and/ornetwork based system upon which applications, operations, and/or otherforms of network resources can be located. For example, system 104 cancorrespond to a service provider, network provider, vehicle securityoperations center (VSOC), content provider and/or medical provider fromwhere services and/or applications can be accessed, sourced or executedfrom. In some embodiments, cloud system 104 can include a server(s)and/or a database of information which is accessible over network 102.In some embodiments, a database (not shown) of system 104 can store adataset of data and metadata associated with local and/or networkinformation related to a user(s) of UE 106, user(s) and the UE 106, andthe services and applications provided by cloud system 104 and/orvehicle 108.

In some embodiments, cloud system 104 can include one or more CPUsconstituting a TEE(s), and one or more GPUs for offloading, as discussedherein. The vehicle 108 can be in communication with the UE 106 vialong-range communication (e.g., satellite or cellular) thru the cloudsystem 104 and network 102, or the vehicle 108 can communicate directlywith the UE 106 over at least medium-range communication (e.g., 802.11Wi-Fi, Bluetooth, Ultra-wide band (UWB).

With the increase demand for hailing services, people are increasinglyrelying on shared mobility drivers (e.g., Uber, Lyft) fortransportation. However, often drivers and riders have difficultiesfinding each other in urban areas. These difficulties include GPSsignals being blocked by buildings such as skyscrapers, attenuated incrowded environments (e.g., in stadiums, airports, bars), at night, andin bad weather. Due to the difficulty in locating each other, theexperience may waste time, creates a bad user experience, and causesmore CO₂ emission due to idling of a vehicle in the system. In thisdisclosure, the use of medium-range communication (e.g., Wi-Fi) is usedto aid drivers, semi-autonomous, and autonomous vehicles in determininga street side of a potential rider or delivery. This system can be alsoknown as CarFi that uses Wi-Fi channel state information (CSI) frommultiple antennas (e.g., 2, 3, 4, etc.) coupled with a moving vehicleand a data-driven technique to determine the street side of the rider,hailer, target. This system was tested by collecting real-world data inrealistic and challenging settings by blocking the signal with otherobject including people, parked cars, etc. Based on these tests, thesystems and methods disclosed had a 95.44% accuracy in rider/target sidedetermination in both line of sight (LoS) and non-line of sight (nLoS)conditions and can be run on a processor such as an embedded processor,or embedded GPU in real-time.

Currently, drivers and riders use smartphones, which rely on GPS orcellular signals, to locate each other while far apart, and require themto recognize each other while nearby. However, in urban cities and areaslike downtown, arenas, and stadiums where there are numerousskyscrapers, GPS signals often do not work. In addition, there areplaces, e.g., airports, malls, hospitals, where the drivers need to cometo a covered area, such as parking garage, to pick up riders where thebuilding/parking structure blocks GPS signals. Also, it is challengingto locate the actual rider among many people in crowded environmentslike stadiums, airports, theatres, and bars. Moreover, the situation canworsen due to lack of visibility, e.g., at night and during bad weather(such as rain, storm, and snow). This issue wastes the time of theriders and drivers, causes more CO₂ emission due to idle driving, andcauses frustration and creates a bad user experience.

A recent Uber study shows that users do not like to negotiate the pickuppoint, and most find it hard to give directions to the driver when theuser is at a new place. Further, many find that determining the streetside that the rider is on a very crucial component. This is because, insome downtown areas, the streets are multi-lane and single directionsuch that if the car is on the other side of the street, the rider mayhave to cross the street, which can be unsafe. See FIG. 2 which is anillustration of a roadway 200 with a vehicle 202 that includes an RFtransceiver to communicate with a riders (targets) mobile phone 206A or206B. Also, the drivers do not want to make a U-turn and realize thatthey were on the right side in the first place, which leads to requiringa second U-turn.

Several solutions have been proposed to improve the rider pick-upexperience. For example, the vehicle can use a camera and facialrecognition to identify the rider and subsequently compute the location.However, facial recognition requires the rider to upload his or herphoto, which can be privacy-invasive. Moreover, for facial recognitionto work, the rider needs to be within the camera's field of view andoccupy enough pixels to be successfully recognized and have goodlighting conditions. One can also ask the user to scan the surroundingswith his or her phone, and then a server can perform 3D reconstructionand matching to the previous established real-world model to compute theexact location of the rider. However, this is a computation-intensiveapproach, and this method also requires the world to be digitized andconstructed to allow such matching. As commercial products, Uber andLyft have multicolored LED-based lights for riders to recognize theircars. However, such a solution does not work in broad daylight, and itis a rider-oriented solution, i.e., the rider has to find the car, andthe driver does not have much information about the location/side of therider.

In this disclosure, one embodiment was a Wi-Fi enabled smartphone and avehicle based Wi-Fi system such as a Wi-Fi enabled dashcam that was usedto determine the street side of a rider (or target or remote system).This system and method can be referred to as CarFi. CarFi neitherrequires the rider to upload any photos of themselves nor the photo ofthe surrounding area, which protects the rider's privacy, reduces thecomputation load, and does not depend on lighting conditions. CarFi usesWi-Fi communications between the rider's smartphone and the vehiclebased Wi-Fi system. The vehicle based Wi-Fi system can be implemented asa standalone devices that can be placed in or installed in any vehicle,or a vehicle that has Wi-Fi already installed.

CarFi uses a multiple antennae (e.g., 2 or 3 antennae that can bearranged in a geometric pattern such as a line, triangle, square, etc.)and a Wi-Fi chipset to receive the Wi-Fi packets sent by the smartphoneheld by the rider. This system does not require any modification to thevehicle or the smartphone. The Wi-Fi packets can be generated by aride-hailing app, which can share the phone's MAC address (or, arandomized MAC address) through the cloud/server to the vehicle (or,driver's app). Thus, the vehicle can listen to the packets generatedfrom the target phone. The system on the vehicle extracts the ChannelState Information (CSI) data from the Wi-Fi chipset. After somepreprocessing, it performs sub-carrier selection. Then, it extractsrelevant features (amplitude difference between antennas, multipathprofile, power delay profile) for rider side determination. Then, thecontextual and motion-related features are encoded into a data-drivenmodel (LSTM) to classify whether the rider is on the right or the leftside of the vehicle. This system and method uses CSI amplitude, howeverit can also be implemented using CSI phase information that may be mademore accurate by implementing phase calibration.

First, a comprehensive exploratory analysis was performed to understandthe potential of using Wi-Fi CSI in an automotive environment for sharedmobility applications. This empirical study involved determining the setof features that can effectively work in an automotive environment inboth line of sight (LoS) and non-line of sight (nLoS) conditions when avehicle is being driven and encoding the features into the design andimplementation of a data-driven model (LSTM) for estimating the side ofthe rider using only two antennas and CSI amplitude. In general a CarFisystem does not require privacy-invasive personal information from therider such as a photo, and avoids heavy computation on the server, andworks in the dark.

Second, was to set up an infrastructure to collect Wi-Fi CSI from amoving vehicle with a done-based system for annotating the ground truthlocation of the vehicle when each packet is received. A datasetcollection of 85 rides with over 568,000 Wi-Fi packets in a realisticand challenging environment, considering both LoS and nLoS, where otherpeople and other parked vehicles block Wi-Fi signals.

Third, based on evaluation using data collected from the real-world, theresults show that CarFi is 95.44% accurate in classifying the rider sidein both LoS and nLoS conditions. By also implementing several baselinesolutions using phase difference and other features illustrated theperformance of this solution. An evaluation of the execution time ofthis approach in both powerful and embedded GPUs was performed and itshowed that this solution can be run on an embedded GPU in real-time.

An overview of the CarFi system 300 is shown in FIG. 3 . When a rider304 wants to travel to a specific location, s/he uses the ride-hailingphone app on the phone to book a trip. The cloud server 306 of theservice providers processes the request and finds a driver. Thelocations of the vehicle 302 and the rider 304 are determined by theirrespective location providers, such as GPS on the phone. Once the tripis confirmed, the driver heads toward the rider's location. As thedriver arrives within a certain distance, e.g., 0.5 miles from the riderbased on the location data, the rider's phone will transmit Wi-Fipackets 308 at a higher transmission rate as the ride-hailing appcontrols it.

In the meantime, the phone's MAC address is shared with the dashcam viathe servers in the cloud. A randomized temporary MAC address can be usedto preserve the privacy of the rider. As the vehicle is also within thiscertain range, the dashcam starts listening for Wi-Fi packets containingthe phone's MAC address and filters out other packets. When CarFi systemreceives the Wi-Fi packets with matched MAC address, it extracts the CSIinformation, performs some preprocessing, and calculates relevantfeatures. Then it feeds the features to an LSTM, which estimates thestreet side of the rider. Then, this information is passed to thedriver's smartphone app from the dashcam for visualization. The dataexchange between the phone and the dashcam can be achieved via eitherBluetooth or cellular connection (if the dashcam has it).

Challenges:

In this section, a discussion of the challenges that a CarFi systemfaced for rider side localization in an automotive environment.

A. Automotive Environment:

When moving Wi-Fi devices from indoor locations to automotiveenvironments, the characteristics of the environment and its effects onthe signals change dramatically. One of the biggest issues in anautomotive environment is the metal structure of the vehicle body, whichcan be similar to a Faraday cage. Although the signal of normal radiofrequency communication systems has a higher frequency than what thewindow can block due to its large size, the vehicle's metal surface canstill block and redistribute the signal. Unfortunately, there has notbeen much work to understand how Wi-Fi CSI looks like inside of avehicle when the vehicle is being driven.

With such a complex RF environment, the current state of-the-art methodcan not accurately estimate the Angle of Arrival (AoA) of the Wi-Fisignal. The X-axis represents the distance of the rider from the car.The car is coming from the left side of the X-axis, meets the rider inthe center, and then leaves. The three antenna arrays are coupled withthe vehicle (e.g., at the center of the dashboard of the car), and theAoA should be 0 to −90° (0 to 90°) when the rider is at the right (left)side. Consider two cases: the rider is standing without anyone blockingthe signal, and two other cars and three other people blocking thesignal. In which the rider was on the right side in both cases. In theLoS cases, the AoA is relatively stable as the Wi-Fi signal penetratesthrough the front windshield, but when the car leaves the rider, thereis a lot of fluctuation of the AoA as the backside of the car blocks thesignal. It was observed that when other people and cars block the rider,the AoA is unreliable even when the rider is in front of the car. SinceAoA estimation also requires three antennas and phase calibration, we donot use AoA in our approach.

B. Speed and Time:

It is not expected that the vehicle will approach the rider at highwayspeed when they are nearby. Instead, it is assumed that the vehicle willbe traveling at a lower speed to be able to stop quickly. Therefore,consider 10 to 20 miles per hour vehicle speed, which translates to 4.47to 8.94 meters per second. Also consider the transmission range of theWi-Fi signal to be around 70 to 120 meters in the outdoor environment.If the rider is 70 meters in front of the car, the driver has about 7.83to 15.66 seconds to stop the vehicle. Given the human response time isabout 1 to 1.5 seconds, it was determined that the if CarFi system takes3 seconds, it will provide adequate time for the driver to respond andstop safely. Smartphones can transmit several hundreds of Wi-Fi packetsin a second. However, there could be a burst of packet loss due tononline of sight (nLoS). In addition, the more time taken to make adecision, the higher accuracy can be offered. Thus, a small window sizewith a variable number of received packets poses a difficult challengefor rider side determination.

C. Cost:

In order to make the solution practical, we need to use inexpensiveantennas and a lightweight computing platform. A simpler solution mightuse two directional antennas to classify left vs. right. However, theneed for directional antennas with 180-degree horizontal beamwidth,which is expensive. For example, an average costs is $225 per antenna.Cheaper ones have a smaller beamwidth. For example, one brand costs$35.94 per antenna, but has only 66 degrees horizontal beam patterns.Also, such directional antennas are bulky and could obstruct the fieldof view of the driver more. Adding more antennas also helps in improvingthe accuracy but also increases the cost of the Wi-Fi chipset andantenna chain. Moreover, the solution needs to be lightweight to be ableto run on an embedded GPU or accelerators. Although, such an acceleratorwould increase hardware cost, a dashcam with such capability couldprovide additional benefits to the drivers by offering additionalservices e.g., detecting accidents, violence/aggression in the car andproviding necessary support by performing audiovisual analysis.

Approach:

FIG. 4 is a flow diagram 400 of an amplitude-based target side of theroad determination. The RF Channel information 402 is extracted from theRF signal and then preprocessed 404 before being fed to an amplitudedifference being determined in block 406. After sub carrier selection isperformed in block 408 followed by classification 410 and lastly theoutput 412 is used to determine if the target or remote device is on theleft or right side of the vehicle.

A. Pre-Processing

When the receiving unit starts to receive Wi-Fi packets, CarFitimestamps each packet and kept all the packets within a window size of3 seconds for processing together. Then, it uses a stride length of 0.4seconds to create the next window. The window size and stride length canbe greater or smaller than the times used.

B. Feature Selection

In this section, a discussion of the set of features that were used forleft vs. right classification.

1) Amplitude difference: The use of Channel State Information (CSI) fromonly two antennas for the classification was used, however multipleantennas can be used. Consider a distance between the antennas being d.In one exploratory analysis, d=5.2 cm, with the CSI containing how theRF signal propagates through the environment as they are being affectedduring transmission. The CSI data collected at the receiver sidecontains those affected and encoded in the complex form with amplitudeand phase information. Each CSI data point is also the Channel FrequencyResponse (CFR):

$\begin{matrix}{{H\left( {f;t} \right)} = {\sum\limits_{n}^{N}{{a_{n}(t)}e^{{- j}2\pi f{\tau_{n}(t)}}}}} & (1)\end{matrix}$

Where a_(i)(t) is the amplitude attenuation factor, τ_(i)(t) is thepropagation delay, and f is the carrier frequency.

A plot of CSI amplitude difference between antenna C and antenna A lookslike for a portion of a ride for 30 sub-carriers, the X-axis showed thedistance of the car with respect to the rider. As the car is approachingfrom the left side of the X-axis, meets the rider at the middle of theX-axis, and then passes the rider after that. When plotting amplitudedifference, plot the CSI amplitude of the antenna C−antenna A, whereantenna A, B, and C are placed from left to right parallel to thedashboard. So, a positive value is a good indicator that the rider is onthe right side. Note that the amplitude difference values fluctuate overtime, and they also vary for different sub-carriers. As the CSIamplitude varies by subcarriers, instead of relying on all thesub-carriers, consider determining the relevant sub-carriers that areless prone to noise.

2) Sub-carrier selection: Instead of relying on all the sub-carriers, weselect sub-carriers that are more resilient to noise. First, we computethe covariance of CSI amplitude of all the subcarriers of antenna C.High covariance between these subcarriers shows they receive effectivesignal and not the noise. For each subcarrier in antenna C, select thecorresponding subcarrier of antenna A. These subcarriers have similarpath properties (e.g., multipath effect, attenuation) and receivecorrelated CSI data. Vary the number of selected subcarriers from 1 to30 and choose the number of subcarriers that provide the highestaccuracy. Please note that sub-carriers are selected per window ofpackets. So, different windows may have different sets of sub-carriers.This approach is a Variance-based Sub-carrier Selection (VbSS). Whenchoosing N subcarriers, consider N−1 sub-carriers using VbSS and add thefirst subcarrier.

C. Multipath Profile

Since Wi-Fi CSI data contains multipath attenuation caused by theenvironment, the multipath profile extracted from the CSI data can bevery useful in location estimation. It can effectively provide whetherthe rider is in LoS or nLoS conditions. To extract the multipath profileof the CSI data, explore how MUSIC and SpotFi algorithms extract signalsand estimate their Angle of Arrival. Inspired by Eigen decomposition ofmatrix XX^(H), where X is the CSI measurement, and X^(H) is theconjugate transpose of X. The eigenvectors and eigenvalues can be usedas features as they are affected by the environment and the vehicle.Taking the top two dominant multipaths and plotting them with both LoSand nLoS conditions, in which the X-axis in both figures shows thedistance from the car as the car is approaching the rider from the leftside of the axis. There was a significant difference between the firstand the second multipath. However, as the car leaves the rider, thebackside of the car blocks the signal and causes nLoS conditions, andhence the difference between the top two multipaths decreasessignificantly.

D. Power Delay Profile

Power Delay Profile (PDP) describes the power level associated with eachmultipath along with the propagation delays. However, due to the limitedbandwidth of the Wi-Fi channels, the path length resolution is not veryprecise. For our 802.11 ac 40 MHz channel, the path length resolution is7.5 m. But it can be helpful for coarse-grained mobility tracking overtime and provide contextual information regarding LoS and nLoS.

When the Wi-Fi chipset measures the channel frequency response aswritten in equation 1, instead of measuring continuously, it samples theresponse at discrete frequency points f=f₀+kΔf where k is thesub-carrier index and Δf=312.5 kHz [33]. Since Equation 1 is in thefrequency domain, by applying Inverse Fourier Transform, we can get theresponse in the time domain which is also the Channel Impulse Response(CIR):

$\begin{matrix}{{f(t)} = {\sum\limits_{n}^{N}{a_{n}{\delta\left( {t - \tau_{t}} \right)}}}} & (2)\end{matrix}$

where a_(n) and N is the same as in Equation 1 and δ(⋅) is the deltafunction. By calculating the norm ∥f(t)∥² of the Channel ImpulseResponse f(t), we can get the Power Delay Profile. Each of the signalsamples in the Channel Impulse Response correlates to differentmultipath as their time to travel from the transmitter to the receiverdiffers due to differences in the traveled length. By considering theIFFT theory, the time resolution Δτ is related to the samplingresolution Δf mentioned above. While increasing the number of bins inIFFT, the actual resolution does not change. As such, set the IFFT binsto the number of subcarriers, which is also the frequency samplingresolution. For collected data, 30 subcarriers were reported for eachantenna. By using two antennas, 60 PDP values were obtained as featuresper Wi-Fi packet. Next show the PDP values from one antenna in LoScondition, and in nLoS condition, where there are three people and twocars blocking the signal between the rider and his car. In both cases,the rider was on the right side. We see how the PDP values are changingas the car approaches the rider from the left side of the X-axis andpasses him. The PDP values do not necessarily tell if the rider is onthe left or right side but help contextualize the packets of similardistance, and in LoS/nLoS conditions to provide additional informationto the classification model.

Classification

Consider different classifiers to classify the side of the rider (leftvs. right), including k-Nearest Neighbor (kNN), Decision Tree (DT), andSupport Vector Machine (SVM). In addition, we design a Long Short-TermMemory (LSTM) neural network classifier by effectively integrating allthe features. Now, we describe the design of an LSTM and how we encodethe relevant contextual and motion-related features. As the vehicleapproaches the rider, the motion of the vehicle, as well as the distancebetween the transmitter (the phone held by the rider) and the receiver(Wi-Fi receiver on the vehicle), provides additional features in thetime domain. For example, Wi-Fi signal differences between differentantennas can vary across time. There are also Wi-Fi signal differenceson the same antenna with different transmitter and receiver distances.Unlike neural network architectures such as Fully Connected NeuralNetwork and Convolutional Neural Network (CNN), LSTM can better encodetime series data with its feedback connections to remember values overarbitrary time intervals. Thus it can exploit the temporal featuresintroduced by the vehicle's motion. While traditional classifiers likekNN, DT, and SVM can capture features at a single time step, they lackthe ability to take into account the temporal features as the signal iscoming from either left or right in both LoS and nLoS situations.

The general execution of LSTM is described in equations below:

it=σ(Wiixt+bii+Whiht−1+bhi)  (3)

ft=σ(Wifxt+bif+Whfht−1+bhf)  (4)

gt=tanh(Wigxt+big+Whght−1+bhg)  (5)

ot=σ(Wioxt+bio+Whoh(t−1)+bho)  (6)

ct=ft⊙ct−1+it⊙gt  (7)

h _(t) =o _(t)⊙tanh(c _(t))  (8)

The main advantage of LSTM to other neural networks in temporal featureunderstanding is the memory cell c_(t), which is used to accumulatestate information in each time step. To decide what to remember andforgot, equation 3 and 4 calculate the input gate and forget gate valuerespectively. The input gate i_(t) decides which information (which iscalculated by Equation 5) is saved to the memory cell. On the otherhand, the forget gate f_(t) control which part of the previous cellstatus could be forgotten. With these calculations, we determine what isthe new memory cell status through equation 7. Additionally, how thememory cell c_(t) propagates to the final state or output h_(t) (throughequation 8) is controlled by the output gate o_(t) (calculated atequation 6). This design allows the LSTM to take into account ofprevious state information and can be self-learned through the trainingprocess. In these equations, x_(t) is the data at time step t, b is thebias in each network connection, the upper case W_(i) and W_(h)represents the matrices of the weight of the input data and recurrentconnection, respectively, and ⊙ is the Hadamard product.

The architecture of LSTM 500 is shown in FIG. 5 . This non-limitedembodiment had an input size of N. 3 LSTM layers are stacked with 256hidden units. They are followed by a linear layer with an input size of256 and an output size of 2. A Softmax layer is added after the linearlayer. The training uses a cyclic learning rate with a 5e-4 initiallearning rate with maximum epochs of 650 with the patience of 200. Forloss function, we use cross-entropy loss. We have a dropout of 0.5 inthe LSTM layers.

The sequence length in LSTM for each sample (or, a window) needs to bethe same. However, we observe burst of packet losses in nLoS conditions.As a result, the number of packets varies from windows to windows (in 3seconds). Hence, the length of the LSTM sequence needs to be determined.We take the median of the number of packets of the windows of thetraining set, which is 855 packets and set that the sequence length ofLSTM. If there are more packets, then we ignore the rest. If there arefewer packets, then we perform zero padding at the end of the sequence.In this way, we actually take 1.5 seconds of Wi-Fi packets half of thetime for the classification.

Before feeding the CSI amplitude difference, power delay profile, andmultipath profile features to LSTM, we normalize them. This is importantto make sure different features with different scales (especially thedominant multipath) do not force the network to weigh differently. So,the features from the multipath profile and power delay profile need tobe crafted in a way that even after normalization, the distinction ofLoS and nLoS does not disappear. In order to ensure that, we create justone feature using the multipath profile by dividing the magnitude of thedominant multipath with that of the less dominant multipath. For thepower delay profile, adding 60 input channels to LSTM may causeover-fitting. So, applying Principal Component Analysis of the 60 PDPfeatures and take the top M principal components to feed to the network.Then vary M from 3 to 5 and show the results in the Evaluation section.After this process, the normalization retains the LoS and nLoSdistinction and reduces the number of input channels to LSTM to reduceoverfitting.

TABLE I Distribution of 85 rides under different conditions. Rider Riderleft ride side side Only Rider 7 6 People both sides of the rider (nocar) 5 6 Two other people blocking signal 13 14 Two cars blocking signal(no other people) 10 12 Two cars and three other people blocking signal6 6

Data Collection

A. System Setup

To extract the Wi-Fi data in an automotive environment, a laptop with anIntel 5300 Wi-Fi Network Interface Card (NIC) was used. The Linux CSItool was used to collect PHY layer CSI information from received Wi-Fipackets. The car is driven with this set up for receiving Wi-Fi packetsabout 10-20 miles per hour. The three antennas are placed in thedashboard and marked as A (leftmost), B (middle), and C (rightmost),when viewed from inside of the vehicle. Although data was collected with3 antennas, only two antennas were used for this embodiment approach(antennas A and C). However more antennas can be utilized. On the riderside, the rider stood with a Pixel 2 XL phone that serves as an AccessPoint (AP) at 5 GHz to which the laptop is connected to. An Android appfrom the phone generates Wi-Fi traffic by pinging the laptop that wasdeveloped and can achieve a packet transmission rate of up to 350packets per second.

B. Collected Dataset

In order to consider realistic scenarios with LoS and nLoS conditions,we collect data of 85 rides under five different conditions: (a) onlyrider standing, (b) people standing on both sides of the rider, (c) twoother people blocking the signal, (d) two other parked cars blocking thesignal, and (e) two other cars and three other people blocking thesignal. We collect data when the rider is on the left and right sides ofthe car in all these conditions. Table I shows the number of rides underdifferent conditions.

The dataset was split into three sections: training (60%), validation(20%), and testing (20%). This was done for each condition and for eachside of the rider. For example, when the rider is at the left side andtwo cars blocking the signal, consider 10 such rides. We take CSI dataof 6, 2, and 2 rides for training, validation, and testing,respectively. In that way, the test set has data of disjoint rides andunder all conditions. For each ride, split the sequence of CSI valuesinto a 3 seconds window with 0.4 seconds stride length. This gives us1032 windows for training, 286 windows for validation, and 285 windowsfor testing.

C. Ground Truth Collection

In order to collect the ground truth of whether the rider is at the leftor right side of the car, one can just record the timestamps of receivedpackets for each side of the rider.

However, we would like to collect the (x,y) location of the car wheneach packet was received to have a better understanding of how the CSIchanges when the vehicle approaches the rider and leaves the rider ateach side. In order to achieve this goal, we use an off-the-shelfconsumer drone hovering above the data collection site to record theprocess. Before the data collection, we first determine landmarklocations (e.g., the rider's location) and four positions that can forma rectangle area with tape-measured ground truth coordinates. Next, weplace solid red-colored papers at each location and on top of the car toenable simple color-based pixel tracking through color thresholding. Inthe recorded video, we use the four locations to perform Homographytransformation so that the pixel plane and real-world plane areparallel. This transformation creates a straightforward translation fromthe pixel coordination system to the real-world coordination systemthrough scaling. Then we can track the vehicle in the pixel domain andinterpolate the real-world (x,y) location through the translation. Priorto each data collection, we also time-synchronize the Android phone, thelaptop with the Intel Wi-Fi chipset, and the drone. Thetime-synchronization between the phone and drone is achieved bycapturing the phone's time with millisecond accuracy at the beginning ofeach drone's video; thus, we can calculate the timestamp based on theframe rate and a reference frame that has the phone's time clearlyrecorded. We also capture a screenshot with both the phone's time(through the laptop's camera) and the laptop's time displayed withmillisecond accuracy; thus, the time difference between them can beeasily calculated. We apply these time offsets to change the timestamprecorded on the laptop and the drone to match the time on the phone fortime synchronization.

Evaluation

The accuracy of CarFi system was estimated and compared withstate-of-the-art methods. Along with an investigation into the effect ofantenna spacing, subcarrier selection, and window size on theperformance of the solution. Also, estimating its execution time, andrange in both LoS and nLoS conditions.

A. Accuracy

This Wi-Fi-based rider side determination technique implemented a fewbaseline methods to compare with our approach in terms of accuracy.

Baseline 1: CSI phase difference based approach: Although our approachdoes not require phase calibration, in order to investigate and comparewith a phase difference based approach, perform phase calibration of theantenna chains with the Intel 5300 chipset attached with the laptop.Another laptop with Intel 5300 chipset was used to transmit Wi-Fipackets through an RF splitter, where all CarFi three receiver antennasare connected to the RF splitter's output. These three antennas shouldreceive the Wi-Fi signal at the same time. However, due to the slightpath distance difference within the RF splitter, we switch the receiverantennas' connecting locations and record the phase information in eachconnection combination to eliminate the difference introduced by the RFsplitter. By removing the offset we measured, we correct the antennaphase offset in our collected data. The system also introduces SamplingTime Offset (STO) and Sampling Frequency Offset (SFO) as the samplingclocks and frequencies are unsynchronized between the receiver and thetransmitter. Then remove STO and SFO through multiple linear regression.

Using only antennas A and C, and estimate the phase difference bysubtracting unwrapped phase A from unwrapped phase C of each window.Ideally, the phase difference should be positive (negative) when therider is on the left (right) side. But for 30 different sub-carriers,the patterns vary significantly. As an example, we show the phasedifference when the rider is at the right side in LoS condition.

Since the unwrapped phase difference change over time, consider fourdifferent ways to compute the features to capture phase differencebetween antennas:

(a) Average all phase differences of all sub-carriers of all packetswithin a window. The intuition is that mean of phase difference shouldbe different for different sides.

(b) Similar to (a), but instead of all the sub-carriers, just use thefirst sub-carrier.

(c) Divide the window into a few sub-windows. The reason forsub-windowing is to reduce the propagation error of phase unwrapping.Then, average all phase differences of all sub-carriers in eachsub-window. Remove 20% sub-windows with large variance. Then, compute apositive or negative vote for each sub-window based on the sign of itsphase difference. Count the numbers of positive and negative votes, anduse them as features. We plot the number of positive and negative votesfor all the 1032 training windows and plot them in FIGS. 11A and 111B.

FIG. 11A is a graphical representation 1100 of negative votes 1102 withrespect to positive votes 1104 of unwrapped phase difference betweenantennas when the rider is on the left side. The green line shows whenthe positive 1104 and negative votes 1102 are the same. It shows thatwhen the rider is on the left side, there are more positive votes.Calculate such votes for all the sub-carriers.

FIG. 11B is a graphical representation 1150 of negative votes 1152 withrespect to positive votes 1154 of unwrapped phase difference betweenantennas when the rider is on the right side. The green line shows whenthe positive 1154 and negative votes 1152 are the same. It shows thatwhen the rider is on the right side, there are more negative votes.Calculate such votes for all the sub-carriers.

(d) After sub-windowing, compute an effective phase difference for eachsubcarrier. The intuition is that phase difference should be stable foreach subcarrier because the central frequency is the same. Choose aneffective phase difference that covers most phase differences within tworadians and has the smallest mean error. An example of such an effectivephase difference is shown in FIG. 12 when the rider is on the right sideand in LoS condition. It shows that even though the phase differencefluctuates, the effective phase difference is negative (as it should besince the rider is on the right side). We estimate like this for 30sub-carriers and use all 30 effective phase differences as features tothe classifiers.

FIG. 12 is a graphical representation 1200 of phase difference 1202 withrespect to number of packets 1204 illustrating a phase difference 1206of subcarrier 1 when the rider is at the right side in LoS and anestimated effective phase difference between antennas (C−A) 1208.

Feed these features to kNN, DT, and SVM classifiers and show the resultsof rider side classification in Table II. For kNN, we vary the value ofk from 3 to 15 and report the accuracy with the best k. It can be seenthat the highest accuracy from the phase difference based approach isonly 56%.

Baseline 2: RSS difference based approach: When we collect data, we alsocollect RSS (Received Signal Strength) values from each antenna. We feedthe average RSS difference of antennas (C−A) of each window to differentclassifiers, including KNN, Decision tree, and SVM, to classify therider side. The results are shown in Table II. The results show that thehighest accuracy is 85.6% that came from both K-NN and SVM. It provideshigher accuracy than the CSI phase difference based approach.

TABLE II Results of left vs. right classification of baseline methods.Baseline KNN (%) DT(%) SVM(%) 1(a) 50.2 (k = 5) 46.0 48.4 1(b) 54.4 (k =11) 50.2 44.2 1(c) 52.6 (k = 3) 52.6 51.6 1(d) 52.6 (k = 3) 56.0 49.5 285.6 (k = 10) 76.1 85.6 3(a) 84.2 (k = 5) 81.1 84.6 3(b) 88.1 (k = 3)83.9 87.7 3(c) 83.5 (k = 7) 81.4 85.3 3(d) 87.3 (k = 8) 82.5 89.5

Baseline 3: CSI amplitude difference based approach:

Since the CSI amplitude difference changes over time, we considerdifferent ways to compute features to capture amplitude difference ofantennas (C−A):

1). We average all CSI amplitude differences of all subcarriers of allpackets within a window.

2). Similar to (a), but we use only the first sub-carrier.

3). Similar to (a), but we also add average RSS difference.

4). Similar to (b), but we also add average RSS difference.

We feed the features to kNN, DT, and SVM classifiers. The results areshown in Table II. Its shows the highest accuracy is 89.5%, whencombining the average CSI amplitude difference and average RSSdifference.

We also implement our LSTM based network and change network parameters,including the size of hidden dimensions and number of layers to see howthat affects performance. The results are shown in Table III. It showsthat when we use our variance based subcarrier selection, the accuracyis higher than when all sub-carriers are used, or only the firstsubcarrier is used. We see that we get 95.44% accuracy when we combinevariance based subcarrier selection, power delay profile, and multipathprofile. This highest accuracy came from when we select 14 subcarrierswith VbSS, obtain 3 PDP features and 1 multipath profile feature. If wefeed the exact same features to kNN, DT, and SVM, we get 68.4%, 69.5%,84.2% accuracy, respectively. Hence, our LSTM based architectureincreases accuracy by 11.24% from exactly the same input.

B. Sensitivity Analysis:

In this Section, we analyze the effect of antenna spacing, subcarrierselection, and window size on CarFi performance.

Effect of antenna spacing: In our analysis, the default antenna spacingwas 5.2 cm, which produced 95.44% accuracy. Since we collected data withthree antennas, we can use antenna A and B to see how the performancelooks like when the antenna spacing is 2.6 cm. We keep the bestperforming network's parameter the same and run the experiment with 2.6cm spacing and find the accuracy is only 55.79%.

Effect of window size: In our analysis, the default window size is 3seconds. We keep the best performing network's parameter the same andrun the experiment by varying window sizes to 0.5, 1, 1.5, 2, 2.5, and 3seconds find the accuracy is 62.95%, 79.36%, 80%, 85.17%, 89.03%,95.44%, respectively. We see that longer windows provide higheraccuracy.

Effect of number of sub-carriers: We keep the best performing network'sparameter the same and run the experiment with changing the number ofsub-carriers from 1 to 16 and show the impact of the number ofsub-carriers through our VbSS method on accuracy in FIG. 9 . When wechoose only one subcarrier, we choose subcarrier 1, as it provides89.47% accuracy. We achieve the highest accuracy (95.44%) when thenumber of sub-carriers is 14.

FIG. 9 is a flow diagram of synchronized phase unwrapping 900 basedtarget side of the road determination. The phase of each antenna isunwrapped 902 a and 902 b and the phase difference is generated 904followed by a comparison between phase differences of the two antenna906 and phase unwrap determination 908 followed by synchronized phaseunwrapping in 910.

TABLE III Results of left vs. right classification when LSTM withdifferent features are used. Description Input Dim Hidden Dim Number oflayers Optimizer Accuracy Variance-based Subcarrier Selection (VbSS) 12256 3 RMSProp 93.33% Variance-based Subcarrier Selection (VbSS) 12 256 3Adam 91.57% Variance-based Subcarrier Selection (VbSS) 12 128 3 RMSProp92.28% Variance-based Subcarrier Selection (VbSS) 12 256 4 RMSProp89.82% Variance-based Subcarrier Selection (VbSS) 12 128 4 RMSProp92.63% First Subcarrier only 1 256 3 RMSProp 89.47% All sub-carriers 30256 3 RMSProp 89.47% VbSS + Multipath Profile (MP) 12 + 1 256 3 RMSProp93.33% VbSS + Power Delay Profile (PDP) 12 + 5 256 3 RMSProp 93.33%VbSS + Power Delay Profile (PDP) 12 + 3 256 3 RMSProp 93.68% VbSS +PDP + MP 12 + 3 + 1 256 3 RMSProp 95.08% VbSS + PDP + MP 14 + 3 + 1 2563 RMSProp 95.44%

C. Execution Time:

We train our LSTM using Nvidia GeForce GTX 1080 Ti GPU. It takes abouttwo hours to train the network. However, the inference is rapid. Weestimate how long it takes to perform inference in a powerful GPU likeNVidia GeForce GTX 1080 Ti as well as an embedded GPU like Nvidia JetsonNano. It takes only 101.77 and 850.37 milliseconds to execute theinference process in 1080 Ti and Jetson Nano, respectively. Hence, thesolution can be run on embedded GPUs in real-time. Also, there areseveral ways to optimize (e.g., recompiling with TensorRT cansignificantly reduce inference time on Jetson devices) and prune themodel to compress the network, which will reduce inference time.

D. Range Analysis:

In this section, we estimate how far CarFi can operate in both LoS andnLoS conditions. We collect additional data for this evaluation. We havea person standing at different distances ranging from 10 meters to 120meters in front of the car in both LoS and nLoS conditions. We transmit10,003 packets from each location. To create a nLoS condition, we have aperson standing between the phone and Wi-Fi receiving unit placed in thecar dashboard. The Packet Delivery Ratio (PDR) at different distancesfrom the car is shown in FIG. 10 . We see that even at a range of 120meters, the PDR is 99.30% in LoS condition. However, in nLoS situation,the PDR drops sharply to 41.65% in 70 meters. At 120 meters, the PDR isonly 0.75%. So, we see that in LoS conditions, CarFi will operate beyond120 meters range.

FIG. 10 is a graphical representation of unwrapped phase of two antennas1000 when a target is at the right side. This is a plot of phase 1002with respect to number of packets 1004 with two lines the first beingunwrapped phase subcarrier 2 antenna 3 1006 and the second beingunwrapped phase subcarrier 2 antenna 2 1008.

Generalizability: Although the data was collected from one large parkinglot, we put an effort to introduce variation in the rides by asking thevolunteers to stand differently to block the signal, move while blockingthe signal, drive at different speeds, and vary the speed in differentrides. As a result, there is a significant variation in the dataset, andwe expect the model to generalize to some extent. One particular reasonwe were not able to collect data from a busy street is that the Wi-Fi ofthe laptop needed to stay connected to the phone for data collectionwith CSI Tool, which is very difficult to obtain in busy streets as thecar can easily go out of the Wi-Fi range. Currently, we are switching toNexmon framework for collecting CSI data, where the phone will injectpackets at a particular Wi-Fi channel. This will allow us to perform alarge-scale data collection from busy streets for testing thegeneralizability of the solution.

Focusing on vehicle side determination.

Hardware set up: A smartphone with Wi-Fi chipset can be used todetermine the street side of the riders. For example, a driver can placehis smartphone in the dashboard of his/her car. Alternately, a dashcamwith Wi-Fi chipset can also be used for this purpose. However, the Wi-Fichipset needs to have at least two antennas to be used using theproposed technique. Having more than two antennas will also work. Asmentioned before, a smartphone/dashcam with Bluetooth or Ultra-wide bandchipset could also be used using the proposed technique. We call thisdevice (smartphone/dashcam) that the driver uses as HardwareX.

Proposed Method: Rider Side Determination Using Wireless Signals

The following steps are taken to help drivers to determine if theirriders are in left vs. right side of the street:

When the driver is at a predetermined distance to the rider (e.g., whenthey are within 0.5 mile radius) this can be detected by GPS signalswith a ride hailing apps, etc.), two actions are taken: 1). Thesmartphone of the rider is configured to transmit Wi-Fi packets, 2). TheHardwareX of the vehicle starts to receive incoming Wi-Fi signals

Wi-Fi has a range of about 100 meters in outdoors. When the driver andthe riders are within this range, one or more Wi-Fi packets generated bythe smartphone of the rider are received by the HardwareX of the driver.

The MAC address of the rider's smartphone is shared with the HardwareXso that HardwareX knows which messages to listen (in other words, whichmessages are coming from the rider's phone). To ensure regulatorycompliance, rider's approval is obtained before sharing the MAC address.Further, to protect security and privacy, a temporary MAC address canalso be assigned to the rider which is used for creating these messages.MAC address can be generated in two ways: 1). The ride app providercreates temporary MAC address which it shares with both rider's phoneand HardwareX. 2). The smartphone creates a temporary MAC address andinforms the ride app which then informs the HardwareX.

When a Wi-Fi packet is received at the HardwareX, the associated ChannelState Information (CSI) is extracted from the physical layer. Itprovides rich information about how a wireless signal propagates fromthe transmitter to a receiver and captures the combined effect of signalscattering, fading, and power decay with distance.

The CSI values are then used to determine the street side of the rider.We propose several methods for this purpose:

FIG. 6A is a graphical representation of amplitude 600 of two antennaswhen a target is at the right side. In this graph, CSI amplitude 602 isplotted with respect to number of packets 604 in which the two lines areantenna 1, subcarrier 1 606 and antenna 2 subcarrier 1 608

FIG. 6B is a graphical representation of amplitude difference 650between two antennas when a target is at the right side. In this graph,CSI amplitude difference 652 is plotted with respect to number ofpackets 654 in which the two lines are amplitude difference 658 and areference line 660

FIG. 7A is a graphical representation of amplitude 700 of two antennaswhen a target is at the left side. In this graph, CSI amplitude 702 isplotted with respect to number of packets 704 in which the two lines areantenna 1, subcarrier 1 706 and antenna 2 subcarrier 1 708

FIG. 7B is a graphical representation of amplitude difference 750between two antennas when a target is at the left side. In this graph,CSI amplitude difference 752 is plotted with respect to number ofpackets 754 in which the two lines are amplitude difference 758 and areference line 760

Amplitude Difference-Based Approach:

There are several ways amplitude difference between antennas can be usedto determine the side of the rider:

-   -   Approach 1: It consists of the following steps:        -   a. CSI data is collected using a sliding window of W seconds            wide with L seconds stride length. Parameters W and L can be            configured. In one embodiment, W was set to 3 seconds and L            was set to 0.4 seconds.        -   b. CSI amplitudes are computed for all Wi-Fi packets on all            subcarriers using complex CSI values. Some sample CSI            amplitudes from two antennas are shown in FIGS. 6A and 7A            when the rider is at the right and left side, respectively.        -   c. CSI amplitude differences are computed for all            corresponding subcarriers of two antennas. Some sample CSI            amplitude differences between two antennas are shown in            FIGS. 6B and 7B when the rider is at the right and left            side, respectively. It shows the amplitude difference is            mostly positive when the rider is at the right side and the            difference is mostly negative when the rider is at the left            side.        -   d. In one embodiment, no subcarrier selection is performed,            and all the subcarriers are used. The average amplitude            difference of all the subcarriers is used as a feature to a            classifier.        -   e. A binary classifier (e.g., KNN, SVM, decision tree) is            used to determine the side of the rider in each sliding            window using the feature computed above.        -   The corresponding computing pipeline is shown in FIG. 4 .    -   Approach 2: It follows similar steps as Approach 1. However, in        step (d), subcarrier selection is performed based on the        variance of CSI amplitude difference using the following step.        -   a. Subcarriers with the largest variance of amplitude            differences are selected, and the average amplitude            difference of all the selected subcarriers is used as a            feature to a classifier.        -   The corresponding computing pipeline is shown in FIG. 4 .    -   Approach 3: It follows similar steps as Approach 1. However, in        step (d), subcarrier selection is performed based on covariance        of the CSI amplitude difference using the following steps.        -   a. The covariances of CSI amplitude of each subcarrier of            the first antenna to every other subcarrier of its own are            computed.        -   b. Subcarriers with the largest covariance are selected.        -   c. For each selected subcarrier of the first antenna,            covariances with CSI amplitude of all subcarriers of the            second antenna are computed.        -   d. Subcarriers are selected from the second antenna without            repetition selection with the highest covariances.        -   The corresponding computing pipeline is shown in FIG. 4 .    -   Approach 4: It follows similar steps as Approach 3. However, in        step (d), subcarrier selection is performed with the permission        to select the same subcarrier of the second antenna for        different selected subcarriers of the first antenna.    -   Approach 5: It follows similar steps as Approach 1. However,        directional Wi-Fi antennas are used instead of omnidirectional        antennas.

Phase Difference-Based Approach

There are several ways phase difference between antennas can be used todetermine the side of the rider:

-   -   Approach 1: It consist of the following steps:        -   a. CSI data is collected using a sliding window of W seconds            wide with L seconds stride length. Parameters W and L can be            configured. In one embodiment, W was set to 3 seconds and L            was set to 0.4 seconds.        -   b. The CSI wrapped phases are computed for all Wi-Fi packets            on all subcarriers using complex CSI values.        -   c. At this step, the phases of CSI values of both antennas            are unwrapped at each subcarrier. In another embodiment, A            synchronized phase unwrapping technique is used that takes            the CSI wrapped phase of the same subcarriers of two            antennas. For each CSI data, if one of the wrapped phase            differences between packets in one antenna is larger than w            and wrapped phase differences between corresponding packets            in the other antenna is larger than π−δ, these two wrapped            phases are unwrapped simultaneously. Otherwise, they are not            unwrapped. In one embodiment, δ is set to 0.2. FIG. 10 is an            example of conventional phase unwrapping, but this can also            be illustrated as synchronized.        -   d. A sub-window segmentation of M Wi-Fi packets is used in            each sliding window. Parameters M can be configured. In one            embodiment, M was set to 100.        -   e. CSI unwrapped phase differences are computed on            corresponding subcarriers of two antennas.        -   f. The average phase difference of all the subcarriers is            used as a feature to a classifier.        -   g. A binary classifier (e.g., KNN, SVM, decision tree) is            used to determine the side of the rider in each sliding            window using the feature computed above.

The corresponding computing pipeline is shown in FIG. 8 .

The synchronized phase unwrapping technique is shown in FIG. 9 .

FIG. 8 is a flow diagram of phase unwrapping 800 based target side ofthe road determination. The channel state information is extracted 802and then preprocessed 804, followed by a phase difference 906 going tosubcarrier selection 808 then a classifier 810, and determination of theside 812.

-   -   Approach 2: It follows similar steps as Approach 1. However, in        step (f), average phase differences of all the subcarriers in        each sub-window are computed. A majority voting is performed for        all average phase differences based on the sign of their values        (positive or negative). The average majority vote is computed        over all sub-windows and used as a feature to a classifier.    -   Approach 3: It follows similar steps as Approach 1. However, in        step (f), the effective phase differences for each Wi-Fi packet        are computed by minimizing the absolute mean error on all the        subcarriers. X percent of outliers (packets) are removed.        Parameters X can be configured. In one embodiment, X was set        to 20. The average of average phase differences over all packets        is computed and used as a feature to a classifier.    -   Approach 4: It follows similar steps as Approach 1. However, in        step (f), the effective phase differences for each subcarrier        are computed by minimizing the absolute mean error on all the        subcarriers. X percent of outliers (subcarriers) are removed.        Parameters X can be configured. In one embodiment, X was set        to 20. The average of average phase differences over all        subcarriers is computed and used as a feature to a classifier.    -   Approach 5: It follows similar steps as Approach 1. However, in        step (f), the average phase differences of all subcarriers are        computed for all sub-windows. X percent of sub-windows with the        largest phase difference variances are removed. Parameters X can        be configured. In one embodiment, X was set to 20. The numbers        of positive and negative average phase differences are counted        and used as features to a classifier.    -   Approach 6: It follows similar steps as Approach 3. However, the        effective phase differences are computed using only phase        differences within U units to themselves. Parameters U can be        configured. In one embodiment, U was set to 2.    -   Approach 7: It follows similar steps as Approach 6. However,        instead of using Approach 3 in step (f), it uses approach 4.    -   Approach 8: It follows similar steps as Approach 7. However, all        average phase differences over all subcarriers are used as        features to a classifier.

Data Driven Approach

There are several ways AI based techniques can be used for rider sidedetermination:

-   -   Approach 1: It consists of the following steps:        -   a. CSI data is collected using a sliding window of W seconds            wide with L seconds stride length. Parameters W and L can be            configured. In one embodiment, W was set to 3 seconds and L            was set to 0.4 seconds.        -   b. CSI amplitudes are computed for all Wi-Fi packets on all            subcarriers using CSI complex raw data.        -   c. CSI amplitude differences are computed for all            corresponding subcarriers of two antennas.        -   d. Subcarrier selection described in Approach A (3) is            performed and S subcarriers are selected. In one embodiment,            S is set to 11.        -   e. A Long Short-term Memory (LSTM) neural network is used as            a binary classifier. In one embodiment, it has input size of            11; 3 LSTM layers with 256 hidden nodes. They are followed            by a linear layer with input size of 256, and output size            of 2. A Softmax layer is after the linear layer.        -   f. For the training hyper-parameters, in one embodiment, the            training uses cyclic learning rate with 5e-4 initial            learning rate; maximum epochs of 1000; patience of 200; and            the cross-entropy loss.        -   g. The amplitude difference series of the selected            subcarriers are used as input of the LSTM neural network and            the output of the LSTM neural network is used to determine            the side of the rider.        -   The corresponding LSTM neural network is shown in FIG. 5 .    -   Approach 2: It follows similar steps as Approach 1. However, at        step (b), (c), and (d), amplitude difference based features and        phase difference based features are used as mentioned above in        approaches A and B.    -   In another embodiment, instead of LSTM, its variants are used,        e.g., Recurrent Neural Networks (RNN), bidirectional LSTM, Gated        Recurrent Unit (GRU) and transformer networks.    -   In another embodiment, a combination of the phase difference and        amplitude difference-based approach or a combination of several        aforementioned methods is used.    -   After the side of the rider is estimated at the driver side,        this information is shared with the smartphone of the rider        using cellular connection. This information is visualized at the        rider's phone in a way that shows the relative side of the car        with respect to the rider.

In another embodiment, instead of just using Wi-Fi, other wirelesstechnologies are used, including but not limited to Bluetooth,Ultra-wideband, or a combination of several.

Experimental Validation: An experimental study to determine the streetside of a rider using Wi-Fi CSI was performed. There were three Wi-Fiantennas mounted on a dashboard. The Wi-Fi antennas were connected to anIntel 5300 chipset, which was integrated into a laptop. For thisanalysis, this antenna array and laptop constitutes HardwareX. The carwas driven several times while the rider is on the left and right sideof the car. The rider was holding a smartphone. There was an Android apprunning on the smartphone that was generating Wi-Fi traffic.

As seen by FIGS. 6B and 7B, when the rider is at the right, theamplitude difference is mostly positive and when the rider is at theleft side, the amplitude difference is mostly negative, respectively.

Rider Proximity Detection Using Wireless Signals

The following steps are taken to help drivers to determine if theirriders are in proximity to their cars:

-   -   When the driver is reaching close to the rider (e.g., when they        are within 0.5 mile radius of each other as detected by the GPS        signals), two actions are taken:        -   A. The smartphone of the rider starts to transmit Wi-Fi            packets        -   B. The HardwareX of the driver starts to listen to incoming            Wi-Fi signals    -   Wi-Fi has a range of about 100 meters in outdoors. When the        driver and the riders are within this range, one or more Wi-Fi        packets generated by the smartphone of the rider are received by        the HardwareX of the driver.    -   The MAC address of the rider's smartphone is shared with the        HardwareX so that HardwareX knows which messages to listen (in        other words, which messages are coming from the rider's phone).        To ensure regulatory compliance, rider's approval is obtained        before sharing the MAC address. Further, to protect security and        privacy, a temporary MAC address can also be assigned to the        rider which is used for creating these messages. MAC address can        be generated in two ways:        -   A. The ride app provider creates temporary MAC address which            it shares with both rider's phone and HardwareX.        -   B. The smartphone creates a temporary MAC address and            informs the ride app which then informs the HardwareX.    -   When a Wi-Fi packet is received at the HardwareX, the associated        Received Signal Strength Indicator (RSSI) is extracted from the        packet.    -   The RSSI value is then used to determine the proximity of the        rider.    -   If the RSSI value is higher than a threshold, T₁ then we        conclude the rider is nearby.    -   The computing pipeline is shown in FIG. 13A.

FIG. 13A is a flow diagram of RSSI-based target range determination1300.

-   -   The proximity information is updated continuously in real-time        as the car moves and newer Wi-Fi packets are received from the        smartphone of the rider.    -   In another embodiment, instead of using a single RSSI from a        single packet, a sequence of N RSSI values from a sequence of N        Wi-Fi packets are processed together to determine the proximity        of the rider. In that case, at first, the RSSI sequence is        denoised using some filtering techniques in the preprocessing        step. Then, for each RSSI, it is checked if the RSSI value is        higher than a threshold T₁. If M number of packets have higher        RSSI values than T₁, then, it is assumed that the rider is in        proximity. The corresponding computing pipeline is shown in FIG.        13B. The δ(condY) function returns 1 if the input condY is        evaluated to be true. Otherwise, it returns 0.

FIG. 13B is another flow diagram of RSSI-based target rangedetermination 1350.

-   -   In another embodiment, instead of showing a proximity, a range        of proximities are visualized. For example, instead of showing        the rider is within a 10 meters radius, the solution shows        whether the rider is within a 10 meters, 20 meters, and 30        meters radius (the numbers are for explanations and subject to        change). This can be done using the aforementioned methods by        having one RSSI threshold T_(i) per radius.    -   After the proximity of the rider is estimated at the driver        side, this information is shared with the smartphone of the        rider using cellular connection. This information is visualized        at the rider's phone in a way that shows the relative proximity        of the car with respect to the rider.    -   In another embodiment, instead of a single antenna, multiple        antennas are utilized. These antennas can be connected to the        radio chip directly supporting multiple antenna connections or        via an antenna switch. The core idea is to collect signal        strengths at different antennas in round robin method. The        proximity decision is then based on one or more of following        strategies:        -   A. Signal strength above pre-determined threshold on any one            of antennas        -   B. Signal strength above pre-determined threshold on all the            antennas    -   In another embodiment, antennas could be one or more directional        antennas. Switching between then can focus on specific location        and providing zone information along with proximity.    -   The RSSI thresholds are adjusted based on the sensitivity of the        Wi-Fi chipsets of riders' smartphones and the drivers'        smartphones or HardwareX.

In another embodiment, instead of just using Wi-Fi, other wirelesstechnologies are used, including but not limited to Bluetooth,Ultra-wideband, or a combination of several.

Experimental Validation:

We perform an experimental study to determine the proximity of a riderusing Wi-Fi Received Signal Strength Indicator (RSSI). For example,consider a rider standing at different distances (1 meter, 3 meter, 5meter, 10 meter, 20 meter, 30 meter) from the car at each quadrant fromthe car (front right, front left, rear right, rear left). As a result,the rider was standing at 24 different locations. It also shows that therider was facing the car with a smartphone in the hand.

During testing, over 111,000 Wi-Fi packets were transmitted from eachlocation. Each Wi-Fi packet was received by an intel 5300 Wi-Fi chipsetattached to a laptop placed inside a car. It had 3 antennas attached toit. It serves as our HardwareX for data collection.

A standard size sedan is about 5 meters. Therefore, a proximity radiusto be 10 meters for this study was appropriate. FIGS. 14A and 14B showthe boxplot of change of RSSI values when the rider is at differentdistances from the car from −30 meters to +30 meters when the rider isat the right side and left side, respectively. These show that if wechoose T₁=−63, the RSSI values are higher than −63 most of the time whenthe rider is within ±10 meters radius in both cases (left and right). Asa result, when a Wi-Fi packet is received, if the RSSI value is higherthan −63 and we declare the rider is nearby, the solution will work ingeneral.

FIG. 14A is a graphical representation of a box plot of RSSI 1400 atdifferent distances between a target and vehicle when the target is onthe right side. This graphical representation illustrates RSSI 1402 withrespect to distance of the target to the vehicle 1404 in a box plotformat.

FIG. 14B is a graphical representation of a box plot of RSSI 1450 atdifferent distances between a target and vehicle when the target is onthe left side. This graphical representation illustrates RSSI 1452 withrespect to distance of the target to the vehicle 1454 in a box plotformat

FIG. 15 is a block diagram illustrating a computing device 1500 (e.g.,HardwareX, UE 106, and a vehicle system 108 such as a vehicleinfotainment system as discussed above) showing an example of a clientdevice or server device used in the various embodiments of thedisclosure.

The computing device 1500 may include more or fewer components thanthose shown in FIG. 15 , depending on the deployment or usage of thedevice 1500. For example, a server computing device, such as arack-mounted server, may not include audio interfaces 1552, displays1554, keypads 1556, illuminators 1558, haptic interfaces 1562, GPSreceivers 1564, or cameras/sensors 1566. Some devices may includeadditional components not shown, such as GPU devices, cryptographicco-processors, AI accelerators, or other peripheral devices.

As shown in FIG. 15 , the device 1500 includes a central processing unit(CPU) 1522 in communication with a mass memory 1530 via a bus 1524. Thecomputing device 1500 also includes one or more network interfaces 1550,an audio interface 1552, a display 1554, a keypad 1556, an illuminator1558, an input/output interface 1560, a haptic interface 1562, anoptional GPS receiver 1564 (and/or an interchangeable or additional GNSSreceiver) and a camera(s) or other optical, thermal, or electromagneticsensors 1566. Device 1500 can include one camera/sensor 1566 or aplurality of cameras/sensors 1566. The positioning of thecamera(s)/sensor(s) 1566 on the device 1500 can change per device 1500model, per device 1500 capabilities, and the like, or some combinationthereof. Device 1500 can include a first wireless transceiver 1568capable of connecting to a first wireless network and a second wirelesstransceiver 1570 capable to connecting to a second wireless network.Here one transceiver may be capable of long-range communication and thesecond may be capable of medium-range communication.

The first radio frequency (RF) transceiver 1568 may comprise atransceiver configured to operate over a wireless network as describedabove in which a “wireless network” should be understood to coupleclient devices with a network. A wireless network may employ stand-alonead-hoc networks, mesh networks, Wireless LAN (WLAN) networks, cellularnetworks, or the like. A wireless network may further employ a pluralityof network access technologies, including Wi-Fi (e.g., IEEE 802.11 andother variants), Long Term Evolution (LTE), WLAN, Wireless Router (WR)mesh, or 2nd, 3rd, 4^(th) or 5^(th) generation (e.g., 3GPP's 2G, 3G, 4G,5G, etc.) cellular technology, mobile edge computing (MEC), Bluetooth,802.11b/g/n, or the like. Network access technologies may enable widearea coverage for devices, such as client devices with varying degreesof mobility, for example. A long-range transceiver includes systemsintended to be capable and practical in communication over a distance ofgreater than 100 meters, examples include satellite and cellular(analog, CDMA, TDMA, 2G, 3G, 4G, LTE, 5G, etc.), while a medium-rangetransceiver include systems designed to operate distances less than 100m, examples include 802.11 Wi-Fi, Bluetooth, Ultra-wide band (UWB).

The second radio frequency (RF) transceiver 1570 may comprise atransceiver capable to connecting to a second wireless network.

In some embodiments, the CPU 1522 may comprise a general-purpose CPU.The CPU 1522 may comprise a single-core or multiple-core CPU. The CPU1522 may comprise a system-on-a-chip (SoC) or a similar embedded system.In some embodiments, a GPU may be used in place of, or in combinationwith, a CPU 1522. Mass memory 1530 may comprise a dynamic random-accessmemory (DRAM) device, a static random-access memory device (SRAM), or aFlash (e.g., NAND Flash) memory device. In some embodiments, mass memory1530 may comprise a combination of such memory types. In one embodiment,the bus 1524 may comprise a Peripheral Component Interconnect Express(PCIe) bus. In some embodiments, the bus 1524 may comprise multiplebusses instead of a single bus.

Mass memory 1530 illustrates another example of computer storage mediafor the storage of information such as computer-readable instructions,data structures, program modules, or other data. Mass memory 1530 storesa basic input/output system (“BIOS”) 1540 for controlling the low-leveloperation of the computing device 1500. The mass memory also stores anoperating system 1541 for controlling the operation of the computingdevice 1500.

Applications 1542 may include computer-executable instructions which,when executed by the computing device 1500, perform any of the methods(or portions of the methods) described previously in the description ofthe preceding Figures. In some embodiments, the software or programsimplementing the method embodiments can be read from a hard disk drive(not illustrated) and temporarily stored in RAM 1532 by CPU 1522. CPU1522 may then read the software or data from RAM 1532, process them, andstore them to RAM 1532 again.

The computing device 1500 may optionally communicate with a base station(not shown) or directly with another computing device. Network interface1550 is sometimes known as a transceiver, transceiving device, ornetwork interface card (NIC).

The audio interface 1552 produces and receives audio signals such as thesound of a human voice. For example, the audio interface 1552 may becoupled to a speaker and microphone (not shown) to enabletelecommunication with others or generate an audio acknowledgment forsome action. Display 1554 may be a liquid crystal display (LCD), gasplasma, light-emitting diode (LED), or any other type of display usedwith a computing device. Display 1554 may also include a touch-sensitivescreen arranged to receive input from an object such as a stylus or adigit from a human hand.

Keypad 1556 may comprise any input device arranged to receive input froma user. Illuminator 1558 may provide a status indication or providelight.

The computing device 1500 also comprises an input/output interface 1560for communicating with external devices, using communicationtechnologies, such as USB, infrared, Bluetooth™, or the like. The hapticinterface 1562 provides tactile feedback to a user of the client device.

The optional GPS receiver or transceiver 1564 can determine the physicalcoordinates of the computing device 1500 on the surface of the Earth,which typically outputs a location as latitude and longitude values. GPStransceiver 1564 can also employ other geo-positioning mechanisms,including, but not limited to, triangulation, assisted GPS (AGPS),E-OTD, CI, SAI, ETA, BSS, or the like, to further determine the physicallocation of the computing device 1500 on the surface of the Earth. Inone embodiment, however, the computing device 1500 may communicatethrough other components, provide other information that may be employedto determine a physical location of the device, including, for example,a MAC address, IP address, or the like.

For the purposes of this disclosure a module is a software, hardware, orfirmware (or combinations thereof) system, process or functionality, orcomponent thereof, that performs or facilitates the processes, features,and/or functions described herein (with or without human interaction oraugmentation). A module can include sub-modules. Software components ofa module may be stored on a computer readable medium for execution by aprocessor. Modules may be integral to one or more servers, or be loadedand executed by one or more servers. One or more modules may be groupedinto an engine or an application.

For the purposes of this disclosure the term “user”, “data owner”,“subscriber” “consumer” or “customer” should be understood to refer to auser of an application or applications as described herein and/or aconsumer of data supplied by a data provider. By way of example, and notlimitation, the term “user” or “subscriber” can refer to a person whoreceives data provided by the data or service provider over the Internetin a browser session, or can refer to an automated software applicationwhich receives the data and stores or processes the data.

In one embodiment, a system for a vehicle includes a multiple antennaradio frequency (RF) transceiver having an identification (ID) and aprocessor coupled with the RF transceiver. The processor is configuredto receive a request, via a first wireless connection, for the vehicleto travel to a location, in response to the vehicle being less than apredetermined distance from the location, receive RF packets, via asecond wireless connection, from a target at the location, identifypackets based on the ID of the RF transceiver, extract channel stateinformation (CSI) from received signals associated with the identifiedpackets, determine an amplitude difference of subcarriers of thereceived signals between each of the multiple antennae, filter noise ofthe amplitude difference of subcarriers based on subcarrier selection toobtain multiple robust amplitude difference signals, and feed themultiple robust amplitude difference signals to a classifier to obtain aside of the vehicle associated with the location of the target.

In another embodiment, a vehicle side target location method includesreceiving a request, via a first wireless connection, for the vehicle totravel to a location, in response to the vehicle being less than apredetermined distance from the location, receiving RF packets, via asecond wireless connection having a multiple antenna radio frequency(RF) transceiver having an identification (ID), from a target at thelocation, identifying packets based on the ID of the RF transceiver,extracting channel state information (CSI) from received signalsassociated with the identified packets, determining an amplitudedifference of subcarriers of the received signals between each of themultiple antennae, filtering noise of the amplitude difference ofsubcarriers based on subcarrier selection to obtain multiple robustamplitude difference signals, and feeding the multiple robust amplitudedifference signals to a classifier to obtain a side of the vehicleassociated with the location of the target.

In another embodiment, a system for performing lane selection associatedwith control of a vehicle, the system includes a multiple antenna radiofrequency (RF) transceiver having an identification (ID), a processor,and a memory including instructions. When the instructions are executedby the processor, it causes the processor to receive a request, via afirst wireless connection, for the vehicle to travel to a location, inresponse to the vehicle being less than a predetermined distance fromthe location, receive RF packets, via a second wireless connection, froma target at the location, identify packets based on the ID of the RFtransceiver, extract channel state information (CSI) from receivedsignals associated with the identified packets, determine an amplitudedifference of subcarriers of the received signals between each of themultiple antennae, filter noise of the amplitude difference ofsubcarriers based on subcarrier selection to obtain multiple robustamplitude difference signals, feed the multiple robust amplitudedifference signals to a classifier to obtain a side of the vehicleassociated with the location of the target, and operate the vehicle tonavigate the vehicle to a lane associated with the side of the vehicle.

In one embodiment, a system for a vehicle includes a multiple antennaradio frequency (RF) transceiver having an identification (ID), and aprocessor coupled with the RF transceiver. The processor is configuredto receive a request, via a first wireless connection, for the vehicleto travel to a location, in response to the vehicle being less than apredetermined distance from the location, receive RF packets, via asecond wireless connection, from a target at the location, identifypackets based on the ID of the RF transceiver, extract channel stateinformation (CSI) from received signals associated with the identifiedpackets, unwrap phase from the CSI of the received signals to obtainsubcarrier phase data, determine a phase difference of subcarriers ofthe received signals between each of the multiple antennae, filter noiseof the phase difference based on subcarrier selection to obtain multiplerobust phase difference signals, and feed the multiple robust phasedifference signals to a classifier to obtain a side of the vehicleassociated with the location of the target.

In another embodiment, a vehicle side target location method includesreceiving a request, via a first wireless connection, for the vehicle totravel to a location, in response to the vehicle being less than apredetermined distance from the location, receiving RF packets, via asecond wireless connection having a multiple antenna radio frequency(RF) transceiver having an identification (ID), from a target at thelocation, identifying packets based on the ID of the RF transceiver,extracting channel state information (CSI) from received signalsassociated with the identified packets, unwrapping phase from the CSI ofthe received signals to obtain subcarrier phase data, determining aphase difference of subcarrier phase data of the received signalsbetween each of the multiple antennae, filtering noise of the phasedifference of subcarriers based on subcarrier selection to obtainmultiple robust phase difference signals, and feeding the multiplerobust phase difference signals to a classifier to obtain a side of thevehicle associated with the location of the target.

In another embodiment, a system for performing lane selection associatedwith autonomous control of a vehicle includes a multiple antenna radiofrequency (RF) transceiver having an identification (ID), a processorcoupled with the RF transceiver, and a memory including instructions.When the instructions are executed by the processor, it causes theprocessor to receive a request, via a first wireless connection, for thevehicle to travel to a location, in response to the vehicle being lessthan a predetermined distance from the location, receive RF packets, viaa second wireless connection, from a target at the location, identifypackets based on the ID of the RF transceiver, extract channel stateinformation (CSI) from received signals associated with the identifiedpackets, determine a phase difference of subcarriers of the receivedsignals between each of the multiple antennae, filter noise of the phasedifference of subcarriers based on subcarrier selection to obtainmultiple robust phase difference signals, feed the multiple robust phasedifference signals to a classifier to obtain a side of the vehicleassociated with the location of the target, and operate the vehicle tonavigate the vehicle to a lane associated with the side of the vehicle.

In one embodiment, a system for a vehicle includes a radio frequency(RF) transceiver having an identification (ID), and a processor coupledwith the RF transceiver. The processor is configured to receive arequest, via a first wireless connection, for the vehicle to travel to alocation, in response to the vehicle being less than a predetermineddistance from the location, receive RF packets, via a second wirelessconnection, from a target at the location, identify packets based on theID of the RF transceiver, extract received signal strength indicator(RSSI) data from received signals associated with the identifiedpackets, filter the RSSI data to obtain a maximum RSSI signal within awindow of time, and in response to the maximum RSSI signal exceeding athreshold, output a signal indictive of the target being less than apredetermined distance from the vehicle.

In another embodiment a system for performing speed control associatedwith control of a vehicle includes a multiple antenna radio frequency(RF) transceiver having an identification (ID), a processor coupled withthe RF transceiver, and a memory including instructions. When theinstructions are executed by the processor, it cause the processor toreceive a location request, via a first wireless connection, from aremote wireless device at a location, in response to the remote wirelessdevice being less than a predetermined distance from the location,receive RF packets, via a second wireless connection, from the remotewireless device at the location, identify packets based on an ID of theRF transceiver, extract received signal strength indicator (RSSI) datafrom received signals associated with the identified packets, filter theRSSI data to obtain a maximum RSSI signal within a window of time, inresponse to the maximum RSSI signal exceeding a threshold, output asignal indictive of the remote wireless device being less than apredetermined distance from the RF transceiver, and operate the vehicleto stop the vehicle based on the signal.

In another embodiment, a method of operating a vehicular infotainmentsystem via a radio frequency (RF) transceiver to locate a remotewireless device includes receiving a location request, via a firstwireless connection, for the remote wireless device at a location,identifying packets based on an ID of the RF transceiver, extractingreceived signal strength indicator (RSSI) data from received signalsassociated with the identified packets, filtering the RSSI data toobtain a maximum RSSI signal within a window of time, and in response tothe maximum RSSI signal exceeding a threshold, outputting a signalindictive of the remote wireless device being less than a predetermineddistance from the RF transceiver.

In one embodiment, a mobile device includes a long-range transceiver, amedium-range transceiver, and a controller. The controller is configuredto receive, from the long-range transceiver, a medium-range IDassociated with a remote medium-range transceiver, transmit, via thelong-range transceiver, an ID, channel, and band of the medium-rangetransceiver, connect, via the medium-range transceiver, to a remotemedium-range transceiver based on the medium-range ID, transmit, via themedium-range transceiver, a beacon of data packets at an interval.

In another embodiment, a method of operating a mobile device includesreceiving, from a long-range transceiver of the mobile device, amedium-range ID associated with a remote medium-range transceiver,transmitting, via the long-range transceiver, an ID, channel, and bandof a medium-range transceiver of the mobile device, and transmitting,via the medium-range transceiver, a beacon of data packets at aninterval.

In another embodiment a mobile device includes a cellular transceiver, aWi-Fi transceiver, and a controller. The controller is configured toreceive, from the cellular transceiver, a Wi-Fi ID associated with aremote Wi-Fi transceiver, transmit, via the cellular transceiver, an ID,channel, and band of the Wi-Fi transceiver, and transmit, via the Wi-Fitransceiver, a beacon of data packets at an interval.

In one embodiment a mobile device includes a long-range transceiver, amedium-range transceiver, and a controller. The controller is configuredto receive, from the long-range transceiver, a target ID associated witha remote medium-range transceiver of a remote system, transmit, via thelong-range transceiver, an ID, channel, and band of the medium-rangetransceiver of the remote system, receive packets from the remotemedium-range transceiver on the channel, extract received signalstrength indicator (RSSI) data from the packets, filter the RSSI data toobtain a maximum RSSI signal within a window of time, in response to themaximum RSSI signal exceeding a threshold, output a signal indictive ofthe remote system being less than a predetermined distance away.

In another embodiment, a method of operating a mobile device via a radiofrequency (RF) transceiver to locate a remote wireless device includesreceiving a location request, via a first wireless connection, for theremote wireless device at a location, identifying packets based on an IDof the RF transceiver, extracting received signal strength indicator(RSSI) data from received signals associated with the identifiedpackets, filtering the RSSI data to obtain a maximum RSSI signal withina window of time, and in response to the maximum RSSI signal exceeding athreshold, outputting a signal indictive of the remote wireless devicebeing less than a predetermined distance from the RF transceiver.

In another embodiment, a mobile device includes a cellular transceiver,a Wi-Fi transceiver, and a controller. The controller is configured toreceive, from the cellular transceiver, a target ID associated with aremote Wi-Fi transceiver of a remote system, transmit, via the cellulartransceiver, an ID, channel, and band of the Wi-Fi transceiver of theremote system, receive packets from the remote Wi-Fi transceiver on thechannel, extract received signal strength indicator (RSSI) data from thepackets, filter the RSSI data to obtain a maximum RSSI signal within awindow of time, in response to the maximum RSSI signal exceeding athreshold, output a signal indictive of the remote system being lessthan a predetermined distance away.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client level or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein may be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible.

Functionality may also be, in whole or in part, distributed amongmultiple components, in manners now known or to become known. Thus,myriad software/hardware/firmware combinations are possible in achievingthe functions, features, interfaces and preferences described herein.Moreover, the scope of the present disclosure covers conventionallyknown manners for carrying out the described features and functions andinterfaces, as well as those variations and modifications that may bemade to the hardware or software or firmware components described hereinas would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described asflowcharts in this disclosure are provided by way of example in order toprovide a more complete understanding of the technology. The disclosedmethods are not limited to the operations and logical flow presentedherein. Alternative embodiments are contemplated in which the order ofthe various operations is altered and in which sub-operations describedas being part of a larger operation are performed independently.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications may be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure.

The program code embodying the algorithms and/or methodologies describedherein is capable of being individually or collectively distributed as aprogram product in a variety of different forms. The program code may bedistributed using a computer readable storage medium having computerreadable program instructions thereon for causing a processor to carryout aspects of one or more embodiments. Computer readable storage media,which is inherently non-transitory, may include volatile andnon-volatile, and removable and non-removable tangible media implementedin any method or technology for storage of information, such ascomputer-readable instructions, data structures, program modules, orother data. Computer readable storage media may further include RAM,ROM, erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory or othersolid state memory technology, portable compact disc read-only memory(CD-ROM), or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium that can be used to store the desired information and which canbe read by a computer. Computer readable program instructions may bedownloaded to a computer, another type of programmable data processingapparatus, or another device from a computer readable storage medium orto an external computer or external storage device via a network.

Computer readable program instructions stored in a computer readablemedium may be used to direct a computer, other types of programmabledata processing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions thatimplement the functions, acts, and/or operations specified in theflowcharts or diagrams. In certain alternative embodiments, thefunctions, acts, and/or operations specified in the flowcharts anddiagrams may be re-ordered, processed serially, and/or processedconcurrently consistent with one or more embodiments. Moreover, any ofthe flowcharts and/or diagrams may include more or fewer nodes or blocksthan those illustrated consistent with one or more embodiments.

The processes, methods, or algorithms can be embodied in whole or inpart using suitable hardware components, such as Application SpecificIntegrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs),state machines, controllers or other hardware components or devices, ora combination of hardware, software and firmware components.

The processes, methods, or algorithms disclosed herein can bedeliverable to/implemented by a processing device, controller, orcomputer, which can include any existing programmable electronic controlunit or dedicated electronic control unit. Similarly, the processes,methods, or algorithms can be stored as data and instructions executableby a controller or computer in many forms including, but not limited to,information permanently stored on non-writable storage media such as ROMdevices and information alterably stored on writeable storage media suchas floppy disks, magnetic tapes, CDs, RAM devices, and other magneticand optical media. The processes, methods, or algorithms can also beimplemented in a software executable object. Alternatively, theprocesses, methods, or algorithms can be embodied in whole or in partusing suitable hardware components, such as Application SpecificIntegrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs),state machines, controllers or other hardware components or devices, ora combination of hardware, software and firmware components.

The present application is related to co-pending application Ser. No.______, filed Jul. 28, 2022 with the titled VEHICLE TO TARGET RANGEFINDER VIA RF POWER, attorney docket number 097182-00160. The presentapplication is also related to co-pending application Ser. No. ______,filed Jul. 28, 2022 with the titled VEHICLE ROAD SIDE IDENTIFICATION OFA TARGET VIA DIFFERENTIAL AMPLITUDE RF SIGNALS, attorney docket number097182-00187. The present application is also related to co-pendingapplication Ser. No. ______, filed Jul. 28, 2022 with the titledREMOTELY ACTIVATED MOBILE DEVICE BEACON, attorney docket number097182-00189. The present application is also related to co-pendingapplication Ser. No. ______, filed Jul. 28, 2022 with the titled MOBILEDEVICE RANGE FINDER VIA RF POWER, attorney docket number 097182-00190.Those applications are incorporated herein by reference in theirentirety, but they are not admitted to be prior art with respect to thepresent application by mention in this cross-reference section.

While exemplary embodiments are described above, it is not intended thatthese embodiments describe all possible forms encompassed by the claims.The words used in the specification are words of description rather thanlimitation, and it is understood that various changes can be madewithout departing from the spirit and scope of the disclosure. Aspreviously described, the features of various embodiments can becombined to form further embodiments of the invention that may not beexplicitly described or illustrated. While various embodiments couldhave been described as providing advantages or being preferred overother embodiments or prior art implementations with respect to one ormore desired characteristics, those of ordinary skill in the artrecognize that one or more features or characteristics can becompromised to achieve desired overall system attributes, which dependon the specific application and implementation. These attributes caninclude, but are not limited to cost, strength, durability, life cyclecost, marketability, appearance, packaging, size, serviceability,weight, manufacturability, ease of assembly, etc. As such, to the extentany embodiments are described as less desirable than other embodimentsor prior art implementations with respect to one or morecharacteristics, these embodiments are not outside the scope of thedisclosure and can be desirable for particular applications.

What is claimed is:
 1. A system for a vehicle comprising: a multipleantenna radio frequency (RF) transceiver having an identification (ID);and a processor coupled with the RF transceiver and configured to:receive a request, via a first wireless connection, for the vehicle totravel to a location, in response to the vehicle being less than apredetermined distance from the location, receive RF packets, via asecond wireless connection, from a target at the location, identifypackets based on the ID of the RF transceiver, extract channel stateinformation (CSI) from received signals associated with the identifiedpackets, unwrap phase from the CSI of the received signals to obtainsubcarrier phase data, determine a phase difference of subcarriers ofthe received signals between each of the multiple antennae, filter noiseof the phase difference based on subcarrier selection to obtain multiplerobust phase difference signals, and feed the multiple robust phasedifference signals to a classifier to obtain a side of the vehicleassociated with the location of the target.
 2. The system of claim 1,wherein the subcarrier selection is all received subcarriers.
 3. Thesystem of claim 1, wherein the subcarrier selection is based on avariance of phase differences of received subcarriers to obtain selectedsubcarriers, and wherein the multiple robust phase difference signals isan average phase difference of the selected subcarriers.
 4. The systemof claim 1, wherein the multiple antenna RF transceiver in configured tosupport at least one of 802.11 (Wi-Fi), Ultra-Wide Band (UWB), andBluetooth (BT).
 5. The system of claim 1, wherein the multiple antennaRF transceiver includes three antennae.
 6. The system of claim 1,wherein the classifier is an LSTM, SVM, or decision tree classifier. 7.The system of claim 1, wherein the target is configured as an accesspoint and the RF transceiver is configured as a client to the target. 8.The system of claim 1, wherein the RF transceiver is configured as anaccess point (Hot spot) and the target is configured as a client to theRF transceiver.
 9. The system of claim 8, wherein the RF transceiverselects a channel and band based on RF traffic to minimize interferenceand transmits the channel and band to the target via the first wirelessconnection.
 10. The system of claim 1, wherein the RF transceiver isconfigured to monitor an ID, channel, and band associated with thetarget.
 11. A vehicle side target location method comprising: receivinga request, via a first wireless connection, for the vehicle to travel toa location; in response to the vehicle being less than a predetermineddistance from the location, receiving RF packets, via a second wirelessconnection having a multiple antenna radio frequency (RF) transceiverhaving an identification (ID), from a target at the location;identifying packets based on the ID of the RF transceiver; extractingchannel state information (CSI) from received signals associated withthe identified packets; unwrapping phase from the CSI of the receivedsignals to obtain subcarrier phase data; determining a phase differenceof subcarrier phase data of the received signals between each of themultiple antennae; filtering noise of the phase difference ofsubcarriers based on subcarrier selection to obtain multiple robustphase difference signals; and feeding the multiple robust phasedifference signals to a classifier to obtain a side of the vehicleassociated with the location of the target.
 12. The method of claim 11,wherein the subcarrier selection is all received subcarriers.
 13. Themethod of claim 11, wherein the subcarrier selection is based on avariance of phase differences of received subcarriers to obtain selectedsubcarriers, and wherein the multiple robust phase difference signals isan average phase difference of the selected subcarriers.
 14. The methodof claim 11, wherein the multi antenna RF transceiver in configured tosupport at least one of 802.11 (Wi-Fi), Ultra-Wide Band (UWB), andBluetooth (BT).
 15. A system for performing lane selection associatedwith autonomous control of a vehicle, the system comprising: a multipleantenna radio frequency (RF) transceiver having an identification (ID);a processor coupled with the RF transceiver; and a memory includinginstructions that, when executed by the processor, cause the processorto: receive a request, via a first wireless connection, for the vehicleto travel to a location, in response to the vehicle being less than apredetermined distance from the location, receive RF packets, via asecond wireless connection, from a target at the location, identifypackets based on the ID of the RF transceiver, extract channel stateinformation (CSI) from received signals associated with the identifiedpackets, determine a phase difference of subcarriers of the receivedsignals between each of the multiple antennae, filter noise of the phasedifference of subcarriers based on subcarrier selection to obtainmultiple robust phase difference signals, feed the multiple robust phasedifference signals to a classifier to obtain a side of the vehicleassociated with the location of the target, and operate the vehicle tonavigate the vehicle to a lane associated with the side of the vehicle.16. The system of claim 15, wherein the memory includes furtherinstructions that, when executed by the processor, cause the processorto perform phase unwrapping after extraction of the CSI.
 17. The systemof claim 15, wherein the subcarrier selection is all receivedsubcarriers.
 18. The system of claim 15, wherein the subcarrierselection is based on a variance of phase differences of receivedsubcarriers to obtain selected subcarriers, and wherein the multiplerobust phase difference signals is an average phase difference of theselected subcarriers.
 19. The system of claim 15, wherein the multiantenna RF transceiver in configured to support at least one of 802.11(Wi-Fi), Ultra-Wide Band (UWB), and Bluetooth (BT).
 20. The system ofclaim 15, further comprising 3 antennae coupled with the RF transceiver.